Commonsense Intelligence: Cracking the Longstanding Challenge in AI

author: Yejin Choi, University of Washington
published: May 3, 2021,   recorded: April 2021,   views: 12

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Despite considerable advances in deep learning, AI remains to be narrow and brittle. One fundamental limitation is its lack of common sense: intuitive reasoning about everyday situations and events, which in turn, requires a wide spectrum of commonsense knowledge about how the physical and social world works, ranging from naive physics to folk psychology to ethical norms. In this talk, I will share our recent adventures in modeling neuro-symbolic commonsense models by melding symbolic and declarative knowledge stored in large-scale commonsense graphs with neural and implicit knowledge stored in large-scale neural language models. I will conclude the talk by discussing the needs for departing from the currently prevalent learning paradigms that lead to task- or even dataset-specific learning, and open research questions for commonsense AI in light of human cognition.

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